Generating Irregularly spaced times

A method for generating time points based on a statistical distribution. The results are stored in the `times` slot of the `utilities` object.

Usage

gentime(x = NULL, ...)

Arguments

x

An object of class `utilities`, or `NULL` (the default). When `x` is `NULL` or omitted, the `utilities` object is created internally, so `gentime(n = 200)` is equivalent to the three-step pattern `o <- utilities(); o <- gentime(o, n = 200); o`.

...

Additional arguments for generating time points:

n

A positive integer. Length of observation times.

distribution

A character string specifying the distribution of the observation times. Default is `"expmixture"`. Available options are: - `"expmixture"`: A mixture of two exponential distributions. - `"uniform"`: A uniform distribution. - `"exponential"`: A single exponential distribution. - `"gamma"`: A gamma distribution.

lambda1

Mean (1/rate) of the exponential distribution or the first exponential distribution in a mixture of exponential distributions. Default is `130`.

lambda2

Mean (1/rate) of the second exponential distribution in a mixture of exponential distributions. Default is `6.5`.

p1

Weight of the first exponential distribution in a mixture of exponential distributions. Default is `0.15`.

p2

Weight of the second exponential distribution in a mixture of exponential distributions. Default is `0.85`.

a

Shape parameter of a gamma distribution or lower limit of the uniform distribution. Default is `0`.

b

Scale parameter of a gamma distribution or upper limit of the uniform distribution. Default is `1`.

Value

An updated `utilities` object with the generated observation times stored in the `times` slot.

References

Eyheramendy S, Elorrieta F, Palma W (2018). “An irregular discrete time series model to identify residuals with autocorrelation in astronomical light curves.” Monthly Notices of the Royal Astronomical Society, 481(4), 4311-4322. ISSN 0035-8711. doi:10.1093/mnras/sty2487 . https://academic.oup.com/mnras/article-pdf/481/4/4311/25906473/sty2487.pdf.

Examples

set.seed(12917)
st=gentime(n=200, distribution = "expmixture", lambda1 = 130,
lambda2 = 6.5, p1 = 0.15, p2 = 0.85)@times
mean(diff(st))
#> [1] 28.4244

st=gentime(n=200, distribution = "expmixture", lambda1 = 15,
lambda2 = 2.5, p1 = 0.15, p2 = 0.85)@times
mean(diff(st))
#> [1] 4.592272